Efficient Feature Selection Model for Gene Expression Data

Abstract

Finding subset of informative gene is very crucial for biology process because several genes increase sharply and most of them are not related with others. In general, feature selection technique consists of two steps 1) all genes is ranked by a filter approach 2) rank list is sent to a wrapper approach. Nevertheless, the accuracy rate for recognition gene is not enough. Therefore, this paper proposes efficient feature selection model for gene expression data. First, two filter approaches are used to define many subset of attribute such as Correlation based Feature Selection (Cfs) and Gain Ratio (GR). Second, wrapper approach is used to evaluate each length of attribute that based on Support Vector Machine (SVM) and Random Forest (RF). The result of experiment depicts CfsSVM, CfsRF, GRSVM, and GRRF based on proposed model produce higher accuracy rate such as 87.10%, 90.32%, 87.10, and 88.71%, respectively.